loveSunning

loveSunning / FastCuda

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FastCuda is a handwritten CUDA operator library featuring progressive GEMM and Reduce kernels, cuBLAS benchmarking, and C/C++/Python interfaces for learning, profiling, and performance optimization.

cudac flash-attention hgemm reduce sgemm
14
0
100% credibility
Found Mar 18, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Cuda
AI Summary

FastCuda is a library of hand-optimized routines for fast matrix multiplication and data reduction on NVIDIA GPUs, with examples, benchmarks, and easy connections to C, C++, or Python.

How It Works

1
🔍 Discover FastCuda

You hear about FastCuda, a handy toolkit that makes your NVIDIA graphics card crunch huge math problems like matrix multiplies super fast.

2
📥 Get the files

Download the project files to your Windows or Linux computer to start using it.

3
🛠️ Prepare everything

Check that your graphics card setup is ready by following the simple requirements list.

4
🚀 Build the tools

Run the easy build steps to create the fast math engines tailored for your card.

5
▶️ Try first example

Launch a sample calculation, like multiplying big grids of numbers, and watch it zoom.

6
🐍 Use in Python

Optionally connect it to Python to do speedy sums or multiplies right from your scripts.

7
📈 Check the speed

Run built-in tests to compare and see how much quicker your results come back.

🎉 Speed boost achieved

Now your heavy number-crunching tasks fly on your graphics card, saving time and power!

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Star Growth

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AI-Generated Review

What is FastCuda?

FastCuda is a CUDA library featuring handwritten kernels for GEMM and reduction operations, delivering six progressive GEMM variants from naive FP32 to Tensor Core HGEMM, plus eight reduction algorithms. It includes cuBLAS benchmarking to measure GFLOPS and bandwidth, with C/C++/Python interfaces for easy integration. Developers get drop-in operators for learning kernel optimization, profiling performance, and running quick benchmarks via CLI tools or Python scripts.

Why is it gaining traction?

Its step-by-step kernel progressions teach real CUDA performance tricks without digging into black-box libs, while built-in cuBLAS comparisons quantify gains on your hardware. Python bindings handle host-to-device transfers seamlessly, and CLI benches like `fastcuda_bench gemm all` spit out timings fast. For modern NVIDIA GPUs, it targets sm_89/sm_120 with TF32/FP16 support out of the box.

Who should use this?

CUDA beginners tuning GEMM/reduce kernels for ML models, performance engineers benchmarking custom ops against cuBLAS, or researchers prototyping on RTX 4090/5060. Ideal for validating handwritten kernels with CPU references and Python experiments before scaling.

Verdict

Worth starring for CUDA learning and profiling—excellent docs, examples, and bilingual guides punch above its 14 stars and 1.0% credibility score. Hold off on production until more adoption; it's raw but constructive for optimization hands-on.

(178 words)

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